Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community—can a limited amount of highly-discriminative (e.g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discrimin...

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Veröffentlicht in:	ISPRS journal of photogrammetry and remote sensing 2019-01, Vol.147, p.193-205
Hauptverfasser:	Hong, Danfeng, Yokoya, Naoto, Ge, Nan, Chanussot, Jocelyn, Zhu, Xiao Xiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Cross-modality Engineering Sciences Graph learning Hyperspectral Manifold alignment Multispectral Remote sensing Semi-supervised learning Signal and Image processing
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